Expert system for online surveillance of nuclear reactor coolant pumps

ABSTRACT

An expert system for online surveillance of nuclear reactor coolant pumps. This system provides a means for early detection of pump or sensor degradation. Degradation is determined through the use of a statistical analysis technique, sequential probability ratio test, applied to information from several sensors which are responsive to differing physical parameters. The results of sequential testing of the data provide the operator with an early warning of possible sensor or pump failure.

CONTRACTUAL ORIGIN OF THE INVENTION

The United States Government has rights in this invention pursuant toContract No. W-31-109-ENG-38 between the U.S. Department of Energy andThe University of Chicago.

BACKGROUND OF THE INVENTION

Reactor coolant pump degradation can produce severe economic penaltiesfor nuclear power plants which have to shut down for extended periods oftime in response to a possible coolant pump failure. As a result, thereis a strong economic incentive to develop and commercialize an effectiveapparatus to provide for early detection of coolant pump problems. Earlydetection of a coolant pump degradation would allow the reactor operatorto manually trip the reactor before major pump damage occurred asopposed to the operator experiencing an automatic rapid shutdown of thereactor due to the loss of coolant caused by a damaged pump or a falsealarm caused by a defective sensor. A controlled response would allowmaintenance to be performed on the pump prior to failure or severedamage or to pinpoint a sensor problem and thus, limit the reactor downtime.

The current general practice is to evaluate the condition of thereactor's coolant pump through the use of high/low limit checks of thepump's operating parameters. Using this system, when the coolant pumpparameters read outside of a zone defined by the high/low values, analarm is sounded, and the pump is shut down resulting in lost operatingtime. This type of analysis can result in a high number of false alarmsand missed alarms when compared to an artificial intelligence techniquewhich more closely analyzes the pump parameters as measured by a set ofpump sensors.

Artificial intelligence techniques in an expert system continuallysurvey and diagnose pump performance and operability as a means ofdetecting the early stages of pump degradation. Since most pumps areequipped with numerous sensors to monitor the condition of the pump, thesensors provide a good data base for use by the expert system.Applicants' expert pump diagnosis system continuously monitors andcompares the digitized signals representing a variety of variablesassociated with the physical condition of the coolant pump: speed,vibration level, power, and discharge pressure. Variation of thesevariables is a possible indication of off-normal operation of the pump.Applicants' invention uses an expert system based on a mathematicalcomparison and analysis of multiple signals from a pair of nuclearreactor coolant pumps to analyze the condition of the coolant pump usingthe aforementioned input signals.

Accordingly, it is an object of this invention to provide an expertsystem for early detection of coolant pump degradation so as to providethe operator with information on this condition prior to pump failure.

A further object of this invention is to provide an expert system fordetermining sensor degradation as opposed to pump failure.

Additional objects, advantages and novel features of the invention willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing or may be learned by practice of the invention. The objectsand advantages of the invention may be realized and attained by means ofinstrumentalities and combinations particularly pointed out in theappended claims.

SUMMARY OF THE INVENTION

To achieve the foregoing and other objectives and in accordance with thepurposes of the present invention, as embodied and broadly describedherein, the present invention provides for a means to determine thedegradation of a nuclear reactor cooling pump and the degradation ofsensors used to measure various parameters associated with the coolingpump. Applicants' invention accomplishes this through the use of anexpert system employing a sequential probability ratio test (SPRT) toevaluate parametric data associated with the function of the coolantpump. The SPRT technique requires the presence of duplicate sensors oneach of two or more pumps. This system provides the reactor operatorwith an early warning system to allow an orderly shut down of the pumpfor sensor or pump degradation in lieu of a rapid emergency shut down ofthe pump.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 represents the expert system for online surveillance of a set ofnuclear reactor coolant pumps.

FIG. 2 depicts the logic diagram for the expert pump-surveillancesystem.

DETAILED DESCRIPTION OF THE INVENTION

The subject invention teaches a expert system and method to determinethe degradation of nuclear reactant coolant pumps and their respectivesensors prior to failure.

FIG. 1 illustrates the architecture of the expert system for an onlinepump-surveillance system. The two coolant pumps 1 and 2 are eachequipped with numerous sensors. A typical sensor arrangement is depictedin FIG. 1 where seven sensors are employed: three which monitor therotor shaft speed 3, two accelerometers 4 which monitor the mechanicalvibration of the pump, a pump power measuring device 5 which measuresthe power needed by the motor to turn the rotor, and a dischargepressure transducer 6 which measures the flow rate of coolant throughthe pump. The information from the sensors is transmitted to the dataacquisition system 7 (DAS) which then interfaces with the artificialintelligence (AI) based inference engine 8. The AI inference engine 8implements an operability logic algorithm illustrated in FIG. 2. The AIsoftware for the inference engine 8 is supported by a layer of utilityroutines which perform generic functions such loading external tables,access to shared knowledge base, interprocess synchronization, andnetwork communication. Output from the AI engine 8 is integrated to acolor-graphics display 9 in the reactor room and is multiplexed back tothe data acquisition system 7 for archive backup storage. If theinference engine 8 detects a degradation in the pump or its sensors anaudible alarm is sounded indicating a pump sensor failure 10 or a pumpdisturbance 11.

FIG. 2 illustrates a flow chart for determining the condition of thecooling pumps through the employment of a sequence of mathematicalalgorithms associated with a series of sequential probability ratio test(SPRT) modules. The input signals 12 are acted on mathematically by asensitive pattern recognition technique, the sequential probabilityratio test (SPRT). The use of the SPRT technique through several if-thensteps provides for early annunciation of sensor operability ordegradation of the coolant pump. Each of the modules 13, 14, 15, and 16employs the SPRT technique to determine the condition of the respectivesensors for the purpose of determining if a problem is sensor or pumprelated. The modules present in the expert system include the shaftspeed SPRT module 13, the vibration level SPRT module 14, the powersignal SPRT module 15, and the discharge pressure SPRT module 16. EachSPRT module is connected to an audible alarm 17 which is sounded when asensor degradation is determined. If no sensor degradation is determinedthe degradation is determined to be due to the pump and the pumpdisturbance alarm 11 is sounded.

Basically, the SPRT modules monitor and compare the signals from twosimilar sensors which respond to a single parameter representing aphysical condition associated with the pump. The purpose of thiscomparison is to identifying subtle changes in the statistical qualityof the noise associated with either signal when compared one to theother. In applications involving two or more reactor coolant pumpsequipped with identical sensors, a SPRT monitor applied to the pumpswill provide a sensitive annunciation of any physical disturbanceaffecting one of the pumps. If each of the pumps had only one sensor, itwould not be possible for the SPRT technique to distinguish between apump degradation event and a degradation of the sensor itself. However,when each pump is equipped with multiple, redundant sensors, the SPRTtechnique can be applied to pairs of sensors on each individual pump forsensor-operability verification.

As is illustrated in the logic diagram of FIG. 2, the expert system issynthesized as a collection of if-then type rules. Each SPRT moduleprocesses and compares the stochastic components of the signals from twosensors that are ostensibly following the same physical process. If anyphysical disturbance causes the noise characteristics for either signalto change, that is a larger variance, skewness, or signal bias, then theSPRT provides a sensitive and rapid annunciation of that disturbancewhile minimizing the probabilities of both false alarms and missedalarms.

The processor 18, of module 13, first interrogates the signals N1 andN2, representing the mean shaft speed for pump 1 and pump 2,respectively. The mean shaft speed signal is obtained by averaging theoutputs of the three RPM sensors 3, FIG. 1 on each pump. If a problem isidentified in the comparison of N1 and N2, a sequence of SPRT tests isinvoked to validate the three sensors on pump 1, signified by A1, B1,and C1. If one of those sensors is identified as degraded, an audiblealarm 11 is actuated. If the three sensors on pump 1 are found to beoperating within tolerance, then the three corresponding sensors on pump2 are tested. If all six sensors are confirmed to be operational,execution is passed to the next SPRT module which in this case is SPRTmodule 14 which tests the vibration-level variable. If these sensors arefound to be operational, then the testing is functionally shifted tomodule 15 the power-signal variable, and then if it is found to befunctioning properly to module 16 the discharge-pressure variable. Thissequential organization is illustrated in FIG. 2. If a problem isidentified in any module, an audible alarm, 10, 11 or 17 is sounded inthe reactor control room, and the operator can initiate a manualshutdown of the reactor to repair the identified problem.

The objective of the AI engine in the applicants' expert system is toanalyze successive observations of a discrete process Y which representsa comparison of the stochastic components of two physical processesmonitored by similar sensors. Let y_(k) represent a sample from theprocess Y at time t_(k). During normal operations with an undergradedphysical system and with sensors that are functioning withinspecifications, the y_(k) should be normally distributed with means O.If the two signals being compared do not have the same nominal meansdue, for example, to differences in calibration, then the input signalswill be pre-normalized to the same nominal mean values during initialoperation.

The specific goal of the AI engine is to declare system 1 or system 2degraded if the drift in Y is sufficiently large that the sequence ofobservations appears to be distributed about means +M or -M, where M isa preassigned system distribution magnitude. The SPRT provides aquantitative framework that enables us to decide between two hypotheses,H1 and H2, namely:

H1: Y is drawn from a Gaussian product distribution function (PDF) withmeans M and variance σ².

H2: Y is drawn from a Gaussian PDF with mean O and variance σ².

If it is supposed that H1 or J2 is true, we wish to decide for H1 or H2with probability (1-β) or (1-α) respectively, where α and β representthe error (misidentification) probabilities.

From the theory of Wald and Wolfowitz, "Optimum Character of theSequential Probability Ratio Test," Ann. Math. Stat., 19,326 (1948), themost powerful test depends on the likelihood ratio l_(n), where ##EQU1##

After n observations have been made, the sequential probability ratio isjust the product of the probability ratios for each step: ##EQU2## whereF(y_(i) |H) is the distribution of the random variable y.

The Wald-Wolfowitz theory operates as follows: Continue sampling as longas

    A<1.sub.n <B                                               (1)

Stop sampling and decide H1 as soon as l_(n) ≧B, and stop sampling anddecide H2 as soon as l_(n) ≦A. The acceptance thresholds are related tothe error (misidentification) probabilities by the followingexpressions: ##EQU3## where

α=probability of accepting H1 when H2 is true (false alarm probability)

β=probability of accepting H2 when H1 is true (missed alarm probability)

Assuming the random variable y_(k) is normally distributed, thelikelihood that H1 is true (mean M, variance σ²) is given by ##EQU4##

Similarly for H2 (means O, variance σ²), ##EQU5## The ratio of equations(3) and (4) gives the likelihood ratio l_(n) ; where l_(n) is expressedas ##EQU6## combining equations 1, 2 and 5, and taking the natural logs,gives ##EQU7## where ##EQU8## then the sequential sampling and decisionstrategy can be concisely represented as ##EQU9##

The SPRT analysis formulated here cannot be applied directly tonon-Gaussian signals. For applications to nuclear system signalscontaminated by non-Gaussian noise, an attempt must first be made topretreat the input signals with a normalizing transformation.

For applications where (a) one requires a high degree of assurance thata system is functioning within specifications and (b) there is not alarge penalty associated with false alarms, it is not uncommon tospecify a B (missed alarm probability) that is much smaller than A(false alarm probability). n safety critical systems one may be morewilling to incur a false alarm than a missed alarm. For applicationswhere a large cost penalty is incurred with any false alarms, it isdesirable to keep both A and B small.

The trade-off that must be considered before one specifies arbitrarilysmall values for A and B is the effect this may have on the sensitivityand maximum decision time needed by the SPRT to annunciate adisturbance. The desired sensitivity of the SPRT is fixed byspecification of M, the system disturbance magnitude. For a given valueof M, the average sample number required to reach a decision isinfluenced by A and B and also by the variance associated with thesignals being monitored. It takes longer to identify a subtle change ina process characterized by a low signal-to-noise ratio than in one witha high signal-to-noise ratio.

The embodiments of the invention in which an exclusive property orprivilege is claimed are defined as follows:
 1. An expert system fordetermining the operability of a specified pump comprising:a set ofpumps of which the specified pump is a member; means for measuringphysical parameters representative to the operations condition each pumpof said set of pumps; means for acquiring data generated by saidmeasuring means; an artificial-intelligence based inference enginecoupled to said data acquiring means where said inference engine appliesa sequential probability ratio test to statistically evaluate saidacquired data to determine a status for the specified pump and itsrespective measuring means by continually monitoring and comparingchanges in a specific operational parameter signal acquired from aplurality of measurement means; means for transferring said statusgenerated by said interference engine to an output system.
 2. The systemof claim 1 wherein said measuring means employs at least two sensors tomeasure a specific physical parameter associated with each pump.
 3. Thesystem of claim 2 in which said measuring means measures a plurality ofphysical parameters.
 4. The system of claim 3 in which said inferenceengine employs said sequential probability ratio test in an orderedpreference for each physical parameter to determine if the specifiedpump or a sensor is degraded.
 5. The system of claim 4 in which anaudible alarm is sounded when said sensor or said specified pump isdetermined to be degraded by said inference engine.
 6. A method forearly determination of pump or pump sensor degradation comprising thesteps of:monitoring physical parameters representative of the operatingcondition of said pump through the use of a plurality of sensors;transmitting said data to a data acquisition system and subsequently toan artificial-intelligence inference engine; statistically analyzing onsaid data arriving at said inference engine through the use of asequential probability ratio test; using said sequential probabilityratio test to establish a status for said sensors and said pump;transmitting said status to an output device.
 7. The method of claim 6which said sequential probability ratio test is conducted in a series ofsequential if-then steps where each sensor is evaluated sequentially todetermine if it is degraded and if said sensor is degraded passing saidstatus to an audible alarm system as well as to an output display and ifsaid sensor is not degraded proceeding to the next sensor.
 8. The methodof claim 7 where if said probability ratio test indicates that thesystem is degraded and if none of said sensors are degraded thendetermining that the pump is degraded and transmitting this informationto an output device and an audible alarm.
 9. The system of claim 1 wheresaid physical parameters include: pump revolutions per minute, pumpvibration measurements, pump power, and pump discharge pressure.